An optimization approach for suggesting product
design parameters based on emotive responses is proposed that
combines Kansei techniques and particle swarm optimization
algorithm (PSO). The approach involves designing a Kansei survey
for collecting data on customers’ affective responses to various
aspects of a product, using several exemplars of the product. After
information gathering, the PSO algorithm is employed to build a
prediction binary linear model that aggregates the survey data.
Subsequently, another binary linear model links product design
parameters to the outputs of the first model to establish mathematical
connections between the subjective impression of a product (Kansei)
and its properties. This approach is illustrated by a case study for pen
design. Three PSO neighborhood configurations are tested, and the
results yield insight into the nature of the optimization function.
Experimental work in implementing the proposed approach was able
to suggest customers’ preferences for pen design attributes that would
be considered optimal by all of those surveyed. They can be used for
improvement and development of new future products.
